There is a known exhaust cleaning device which is provided with a particulate filter (hereinafter abbreviated as DPF) for collecting particulates (hereinafter abbreviated as PM) ejected from a diesel engine. In such a device, a DPF temperature is increased to, for example, 600 degrees centigrade or more when the amount of PM accumulated on the DPF reaches a predetermined value, in order to burn and remove the PM on the DPF during a DPF regeneration process.
At this time, post injection, delay in fuel injection timing, intake throttle, or the like is used as a method for increasing the DPF temperature, but any of these methods have the problem of reduction in fuel efficiency. The higher the temperature, on the other hand, the higher the burning rate of the PM becomes and, hence, the regeneration is completed in a short time. Thus, reduction in fuel efficiency in accordance with the DPF regeneration becomes low. If the DPF temperature is too high, however, the DPF temperature suddenly rises due to the sudden burn of the PM. This present a possibility of breakage of the DPF, degradation in a catalyst supported on the DPF, and the like. Accordingly, to restrain reduction in fuel efficiency and safely regenerate the DPF, it is necessary to control the temperature in such a manner as to maintain the DPF temperature at approximately a target temperature suited for the regeneration.
However, there is limitation in the ability to increase temperature of temperature increase means, and dispersion occurs in accordance with each operational state. Thus, exhaust temperature and the DPF temperature vary. To suppress these variations, it is necessary to correct the operational amount of the temperature increase means (for example, the injection quantity of the post injection), and quickly converge the DPF temperature to approximately the target temperature suited for the regeneration.
Conventional technologies include exhaust temperature feedback control in which the exhaust temperature detected by a sensor or the like is fed back and the operational amount of the temperature increase means is corrected in accordance with a deviation from the target temperature in order to maintain the DPF temperature at approximately the target temperature (see, for example, Japanese Patent Laid-Open Publication No. 2003-172185).
At this time, the exhaust temperature corresponding to the commanded operational amount has dispersion. Taking the case of using the post injection as the temperature increase means, dispersion occurs in the exhaust temperature, because actual injection quantity is different from commanded post injection quantity due to injector-to-injector variation and the like, and heat of reaction of HC is reduced due to degradation in the catalyst with a lapse of time. In the feedback control, there is a problem that the dispersion in temperature exacerbates control performance.
Accordingly, learning control with the use of the correction amount of the feedback control is considered. The feedback control is first carried out, and the dispersion in temperature is detected from the correction amount of the temperature increase means when the exhaust temperature stabilizes at approximately the target temperature. This correction amount is stored (learned) in a memory as a learning amount on an operational state basis. The amount of temperature increase operation is corrected with the learning amount corresponding to the operational state. Therefore, in the operational state which has learned once, correction corresponding to the dispersion in temperature can be carried out in advance, so that it is possible to improve the control performance.
In this method, however, there is a problem that learnable operational states are limited. When the exhaust temperature is sufficiently stable, as shown in FIG. 15, the dispersion in temperature can be detected from the correction amount (a steady state in the drawing). In a transient state, in which variation in the exhaust temperature per time is large, however, the correction amount largely varies too. Thus, it is impossible to detect the dispersion in temperature from the correction amount in the transient state and, hence, it is necessary to learn the correction amount in a state where the exhaust temperature sufficiently stabilizes with respect to the operational state and the amount of temperature increase operation. Variation in the exhaust temperature with respect to variation in the operational state and the amount of temperature increase operation is extremely sluggish (for example, 63% response takes 5 to 60 seconds). Furthermore, the operational state frequently varies in driving a vehicle. Therefore, the exhaust temperature comes in the steady state with extremely low frequency.
When the learnable operational state is limited to the steady state, as described above, it is impossible to obtain sufficient learning frequency and, hence, learning with high precision becomes difficult.